{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Pyplot 模块介绍\n",
    "\n",
    "每个Pyplot函数都负责一项工作，比如创建图形，在图形上创建一个绘图区，在绘图区上添加线条、图像标签等。\n",
    "\n",
    "## 1.1 plot()函数\n",
    "\n",
    "绘制一条直线，并添加y轴标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'numbers')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4])\n",
    "plt.ylabel('numbers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot() 是一个非常灵活的函数，可以接受不同个数的参数（多态的体现）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f66a8d6fe10>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')  # 红色圆点 \n",
    "plt.axis([0, 6, 0, 20])  # 坐标范围(x最小，x最大, y最小, y最大)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f66a85c5fd0>,\n",
       " <matplotlib.lines.Line2D at 0x7f66a85d3160>,\n",
       " <matplotlib.lines.Line2D at 0x7f66a85d3278>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据是Numpy数组，不同风格的线条\n",
    "t = np.arange(0, 5, 0.2)\n",
    "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线条的属性：\n",
    "\n",
    "- 关键字参数：\n",
    "  `plt.plot(x, y, linewidth=2.0)`\n",
    "- 使用plot()函数返回的Line2D对象进行设置：\n",
    "  ```\n",
    "  line, = plt.plot(x, y, '-')\n",
    "  line.set_antialiased(False)\n",
    "  ```\n",
    "- 使用setp()函数：\n",
    "  `plt.setp(lines, 'color', 'r', 'linewidth', 2.0)`\n",
    "  \n",
    "如果图形里面包含多个线条，可以先解包在设置相应的属性：\n",
    "\n",
    "line1, line2 = plot(x1, y1, x2, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 绘制子图\n",
    "\n",
    "gca()函数用于返回当前的Axes，gcf()函数用于返回当前的图形。\n",
    "\n",
    "figure()函数是可选的，会默认自动创建的。subplot()函数的3个参数(numrows, numcols, fignum)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#定义一个函数输出值为指数函数和余旋函数的乘积\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "#添加子图\n",
    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
    "plt.subplot(212)\n",
    "#添加子图\n",
    "plt.plot(t2,np.cos(2*np.pi*t2), 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除可以再一张图形上绘制多张子图外，还可以重复使用figure()函数创建多个图形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shilinlee/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel_launcher.py:10: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Easy as 1, 2, 3')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)  # 创建第一张图\n",
    "plt.subplot(211)  # 第一张图形里面的第一张子图\n",
    "plt.plot([1,2,3])\n",
    "plt.subplot(212)  # 第一张图形里面的第二张子图\n",
    "plt.plot([4,5,6])\n",
    "\n",
    "plt.figure(2)  # 第二个图形\n",
    "plt.plot([4,5,6])  # 默认创建 plt.subplot(111)\n",
    "plt.figure(1) # \n",
    "plt.subplot(211)\n",
    "plt.title('Easy as 1, 2, 3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 添加注释\n",
    "\n",
    "text()函数可以再图形上的任意位置添加文本。xlabel()、ylabel()和title()函数分别用于在x轴、y轴和图标题处添加文本。annotation()函数可以非常方便地在图形的特定位置添加文本。\n",
    "\n",
    "**设置注释的字体。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 其他坐标轴类型\n",
    "\n",
    "Matplotlib除可以绘制线性坐标轴图形外，还可以绘制以对数、分对数为坐标轴的图形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(19680801)\n",
    "#随机数种子\n",
    "y = np.random.normal(loc=0.5, scale=0.4, size=1000)\n",
    "#生成均匀分布的数组\n",
    "y= y[(y > 0) & (y < 1)]\n",
    "#筛选出在0到1之间的y值\n",
    "y.sort()\n",
    "#对y值排序\n",
    "x = np.arange(len(y))\n",
    "#使用range函数生成一个和y数组长度一样的序列\n",
    "plt.figure(1)\n",
    "# linear\n",
    "plt.subplot(221)\n",
    "#添加子图\n",
    "plt.plot(x, y)\n",
    "#绘制子图\n",
    "plt.yscale('linear')\n",
    "#设置y轴为线形\n",
    "plt.title('linear')\n",
    "#添加标题\n",
    "plt.grid(True)\n",
    "#使用网格线\n",
    "#log\n",
    "plt.subplot(222)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('log')\n",
    "plt.title('log')\n",
    "#设置y轴为log\n",
    "plt.grid(True)\n",
    "# symmetric log\n",
    "plt.subplot(223)\n",
    "plt.plot(x, y - y.mean())\n",
    "plt.yscale('symlog', linthreshy=0.01)\n",
    "#设置y轴为symlog\n",
    "plt.title('symlog')\n",
    "plt.grid(True)\n",
    "# logit\n",
    "plt.subplot(224)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('logit')\n",
    "plt.title('logit')\n",
    "#设置y轴为logit\n",
    "plt.grid(True)\n",
    "plt.gca().yaxis.set_minor_formatter(ticker.NullFormatter())\n",
    "plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,wspace=0.35)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  2 应用 Pyplot 模块\n",
    "\n",
    "plot()函数接收一个或者多个x,y值对，生成一系列的点，并且可以通过setp()函数来设置这些点的大小。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#定义生产y值的函数\n",
    "def f(t):\n",
    "    s1 = np.cos(2 * np.pi * t)\n",
    "    e1 = np.exp(-t)\n",
    "    return s1 * e1\n",
    "t1 = np.arange(0.0, 5.0, .2)\n",
    "l = plt.plot(t1, f(t1), 'ro')\n",
    "plt.setp(l, 'markersize',30) #设置数据点的大小\n",
    "plt.setp(l, 'markerfacecolor', 'b') #设置数据点的颜色\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在绘制图形的时候，可以绘制网格线。轴的标记长度以及轴标签的颜色和大小也可以调整。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = np.sin(2 * np.pi * t)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, s)\n",
    "ax.grid(True)\n",
    "ticklines = ax.get_xticklines() + ax.get_yticklines()\n",
    "gridlines = ax.get_xgridlines() + ax.get_ygridlines()\n",
    "ticklabels = ax.get_xticklabels() + ax.get_yticklabels()\n",
    "#设置轴的标记的长度\n",
    "for line in ticklines:\n",
    "    line.set_linewidth(3)\n",
    "#设置网格的风格\n",
    "for line in gridlines:\n",
    "    line.set_linestyle('-.')\n",
    "#设置轴标签的颜色和大小\n",
    "for label in ticklabels:\n",
    "    label.set_color('r')\n",
    "    label.set_fontsize('medium')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bar()函数来绘制柱状图。 \n",
    "\n",
    "在绘制p2的时候吧bottom参数设置为menMeans，p2就可以直接堆叠在p1上面；还使用了legend()函数来设置图例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.font_manager as fm\n",
    "font = fm.FontProperties(fname=\"/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf\")\n",
    "N = 5\n",
    "menMeans = (20, 35, 30, 35, 27)\n",
    "womenMeans = (25, 32, 34, 20, 25)\n",
    "menStd = (2, 3, 4, 1, 2)\n",
    "#men的偏差值\n",
    "womenStd = (3, 5, 2, 3, 3)\n",
    "#women的偏差值\n",
    "ind = np.arange(N)\n",
    "#各个组的X轴坐标\n",
    "width = 0.35 #柱状图里面线条的宽度\n",
    "p1 = plt.bar(ind, menMeans, width, color='#d62728', yerr=menStd)\n",
    "p2 = plt.bar(ind, womenMeans, width, bottom=menMeans, yerr=womenStd)\n",
    "plt.ylabel('score', fontproperties=font)\n",
    "plt.title('score in different sex', fontproperties=font)\n",
    "plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))\n",
    "plt.yticks(np.arange(0, 81, 10))\n",
    "plt.legend((p1[0], p2[0]), ('boy', 'girl'), prop=font)  #设置图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同数据组的数据对比。\n",
    "\n",
    "本例采用不同的颜色区分不同组别数据。第二组的x轴的坐标：第一组的x轴坐标加上柱体宽度的一半。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_groups = 5\n",
    "means_men = (20, 35, 30, 35, 27)\n",
    "std_men = (2, 3, 4, 1, 2)\n",
    "means_women = (25, 32, 34, 20, 25)\n",
    "std_women = (3, 5, 2, 3, 3)\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35 #设置bar的宽度\n",
    "opacity = 0.4 #设置透明度\n",
    "error_config = {'ecolor': '0.3'}\n",
    "#绘制柱状图1\n",
    "rects1 = plt.bar(index, means_men, bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "yerr=std_men,\n",
    "error_kw=error_config,\n",
    "label='boy')\n",
    "#绘制柱状图2\n",
    "rects2 = plt.bar(index + bar_width, means_women, bar_width,\n",
    "alpha=opacity,\n",
    "color='r',\n",
    "yerr=std_women,\n",
    "error_kw=error_config,\n",
    "label='girl')\n",
    "plt.xlabel('group',fontproperties=font)\n",
    "plt.ylabel('score', fontproperties=font)\n",
    "plt.title('different sex, score in group', fontproperties=font)\n",
    "#设置X轴标签的位置\n",
    "plt.xticks(index + bar_width / 2, ('A', 'B', 'C', 'D', 'E'))\n",
    "plt.legend(prop=font)#设置图例的字体\n",
    "plt.tight_layout() #分层\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "饼图可以直观地表达各个部分的总体比例。使用matplotlib的pie()函数可以非常方便地绘制饼图。\n",
    "\n",
    "本例通过explode参数分离饼图的一部分，并自动标记各个部分的比例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [15, 30, 45, 10]\n",
    "explode = (0, 0.1, 0, 0)  # 分割出第二个切片(比如'Hogs')\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # 保证饼图绘制出来后是圆形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "散点图可以直观地表达数据点的分布规律。matplotlib的scatter()函数来绘制散点图。\n",
    "\n",
    "本例采用marker参数定义散点图的类型，并且定义了图例的位置。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f66a7fb7dd8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0.0, 50.0, 2.0)\n",
    "y = x ** 1.3 + np.random.rand(*x.shape) * 30.0\n",
    "s = np.random.rand(*x.shape) * 800 + 500\n",
    "plt.scatter(x, y, s, c=\"g\", alpha=0.5, marker=r'$\\clubsuit$', label=\"tree\")\n",
    "plt.legend(prop=font,loc=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三维图形可以在更高的纬度全面地展示数据。matplotlib在绘制三维图的时候需要第三方的支持。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MSQnf/GaIe+6BSKRH4QxtxGI+uruhuxuee07g/vsHgT7c7jC1tWlqa8NccskJLrmklte/vhaHY3biZoToGmXp+v1+QzfS9IgLNkV3GqZKaBgbG6O1tVVuh5N5kCUrVE0IkySiSk9sNaIrCAKDg4OEw2EGBgbYunUrJSUliudW7Ojlg5XClsrLy+WsQZh+Zz1TiKVsLGkuP/1pgJtv7mNkJDLruYnYiEZL6eqCrq5yDh1K86UvDQA9lJYOcNttldx4415NI88mrXi6MY2wdCcmJnQPbayvr6evr4/FixfT19eXlfCjBVN08yDF/o2MjGT5OaWsGo/Hk9UOJxObzTapp9VMSEKt9CRU4l4QBIGBgQHa2tooKyvD4/GwZcsWVfMymUyuiE+3s55Zwauzs5N4PM6xYy4+/3kXfX3Ko0+0EwJeJRBoo7PzdZpHMSLZwij3QmY1NL248sor+d73vsett97K9773Pd7xjnfMajxTdDOQwr5SqRTpdJoTJ06we/duOXusoqKCCy64YFpLUYu/VW0theksXal9T1tbGxUVFVx00UW43e6iikaY7+16lJCb8HH6dJx/+qdeTp6cAIwW3DhwEjgFiOeJ16s9k6+Y3Atq7zJzufbaa3n22WcZHh5m6dKl3Hnnndx6661cc801fPvb32bFihU8+uijs5qjKbrkT2hIp9PE43EOHDhATU3NtO1wMtEiulpCwHJFN51O09/fT3t7O5WVlWzfvj2rkSEUR4WsYkDLb3jvvQlOniwF3EAi56FtU3QyKeAMcAJReM/h88187k45apFspOlxof7Rj36U9/mnn3561mNLnNeiO5XYdnV10dPTg8Vi4aKLLlLlA7Xb7ZrdC1ren06n6e3tpaOjY9qLg1q/McydSM93S1fLb5JICIAVcJ19ZJJGFElRhD2eGJFIMM/7ppwV0AkcRXQpTMbnW/iWrnTOzHfD4rwU3Xwxtslkko6ODgYHB2loaGDv3r0cPXpU9QHU6l5Qa+mmUim6urro7OyktraWnTt3ThsIrlZ0M5sXKn3/fGU+FKjJqdGSgxXRAhbvTCLy3loAGAWCQPLs+zxA5nEeAI6cfd/UlJa6p319OozaSFNy56iGaDRaFJvE55Xo5hPbWCxGW1sbY2NjLF++nL1798on2Gw2xYz6TDqdpru7m1AoRDQanVFsJbQWvZmLdNL5XopRy/ySSS3zKD37yGUCUWQPI7oTZmZ0VNxUzWzbrvTYZrb/0Qsj3Avj4+PzurqYxHkhuvlibMPhMG1tbQQCAVatWpXVDkfCbrcbbrVKn5lJEFOpFN3d3XR3d1NfX4/X682q0DQTsy3vWGgWnntBz1mUn338WfEn1qxZjtfrJRQKTUr4yAxry1dU3gir1Aj3ghGRC0awoEVXEttIJMLx48dpamrKaoezatUqNm/ePOUi0uKftdvtqorRwPRCLbkRuru7Wbx4sVw0Z3BwUNV3GFmDNx6P09PTg9vtVp0cMBXzWXS1oK/oSii/uNfXV0yKL5WKyodCIfx+P319fVkJH5IQS2119MQIS3diYmLeVxiDBSi6+RIaHA4H0WiUQ4cOAczYDkeiEJtikD96IZlMyht6DQ0N7NmzZ1aWgRGWbiKRoL29ncHBQerq6piYmKCnp4dYLCYXI8m0opSG8sxn/zBom5+4kaY3ys/NigrPpOemKyovdfgYGxtjZGSE0dFROjo6siq15SZ8qMEIS3d8fNy0dAvJVB0aRkZGaGtrIxqNsnXrVlU+n0JsiuV+RtrQ6+vrY+nSpezdu1cXi0BPSzeRSNDR0cHAwAArVqxgz549WX5y6e+Q6hdk1rx1Op2TxNhIv/F8KcWo8tqtEOXnWXn5ZNGdityEj2QyyZIlSygpKZHFeGRkRE74kIrKZ2bgzXSBNSIiwnQvFIjchAZpgQ0ODtLe3o7P52Pz5s0cPXpUtZO9UJauzWYjEonQ3NzMwMCAYrE1sl5DPktXuiD09/ezbNmyrE3H3L5m+QqQC4KQVfO2q6uLcDhMOp3O8i1Kx3I+Mj98uhLKz83y8tnH6U5VZDy3F1pbW1tWY8rMC6xk3RrhXjCi2I0RFK3o5ouxFQSB3t5eOjs7qaysZNu2bZMSBNSgxT+rVqjj8TgDAwOMjo6yZs2aLCGbDi0hYFot3WQySWdnZ5b1rcXas1gsuFwuXC5XVpnF3O7BY2NjjI2NyYs20zKe6x5p2qMXDJiMCkt3NgI3U8jYdEXlJTHu7+8nGAySSqVwOp1Eo1EGBgZmXVQ+k4mJCbka2Hym6ER3KrGVNpty2+HkflbNgjEyZCwej9Pe3s7Q0BDV1dUsWbKE5cuXK/4eI4vkSO9PJpO0tbXR29sr+5WVfF8olObBBzvZsMHBvn0z13/N7R6cTqeprKykoqJCdlEMDw/LPdIy2/JI/zWylGYmWjfSUqm58+nO1rui1RWQr0uwdLdz6NAhEonElEXlpX5oar7XdC8YRCqVIpFIKG6HIyFZoGryso0IGYvFYrS3tzMyMsKKFStYs2YNExMT9PX1qf4etSKqVDBSqRTBYJBXX31VVeeLWCzNv/3bMD/60XDGxWoAtztCfX2K9eud7NxZxuWX13PRRVVTXgCl53PrF0hkuigyF21u8XEjAuWL0dLVo+avXhc16W7HZrNlGRm5ReVHR0dl15N0XKVjO1WMsSm6BiE1ZVTbDsfhcGgSXb0s3Wg0KidhrFy5MqurxFzVa8hFSrzo6urCZrOxadOmrALfU5FIpPm3fxvioYcGSSRyfy8b0aiPjg7o6IDf/S7Bf/93NxZLK6WlUZYuhS1bPPzVX1Xy5jcvZsmSmYUyX1ue3OLjAwMDRCIROSGms7Mzy0UxGyGaP6KrbFCrdfaiq+dmZ74Ll5Ki8qFQiOHhYcLhMECWGA8ODpqiaxQTExMcOXJkknDNRCHDvzItymg0SmtrK+Pj41MmYWj9nlwRfemlCLfe2oogDNHUVMYb31jHG96wCJfLPq3optNpenp66OzsZNGiRezevZvm5uYZxSWVSnP77UN8/et9xGLqdooEwYnf7+TkSTh5Eh59NAicxm6PUF4eYeXKLnburOTSS2u47LJ6uSvCVExVfDwYDHLmzBkcDgdjY2N0dXXJO+65LgolIUxaLV2NjT6mIY1Yc2FmZiu6oG8Yn5q04pmKykvJHrfffjuvvfYa733ve9myZQsXX3wx//iP/6h5jl/60pf43//9XywWCxdccAHf/e53Z7U/lEnRiW5ZWRl79+5VfRLY7XZVbculz6gVaolIJEJrayt+v59Vq1axcePGKec82zCzl1+OcMMN3Zw8OX72VS9//nOKBx7oA7ooKYlQWxtn7Vo7+/aN87a3LWH9+oqsYjl1dXXs2rVLvhOYqXzkpz89zJe+1EskMm1RAZVYSCZLGBkpYWQEXnkFHnzwVaqrf05b279oGtFqteJwOCZtsCQSiUmdg1OplOxXzHRRZAqEdtHV26er/Ly02eZXZ2A9YnRz9wEee+wx9u3bx1NPPUVHRwdjY2Oax+7p6eH+++/n5MmTeDwerrnmGh555BH+/u//flZzlig60bVarZpOeq3ZZWo/Ew6HiUQiHD16lMbGRjZt2jTjfNX6Z0H8HQ4divHv/36GEyfGp3mnnXC4VL69f+qpEJ/4xBms1ig+X4ilSwV27arkssscLFokIHlf8oWMCYLA5z8/wj339BIKGV0Tdgg4BgwSjeofseBwOKisrJy0ySP5FaXNO+lWVsrQknqjqRVf/S1d5QPa7fNLdI0qYJ5IJCgrK2Pr1q2zHkvKZHU4HITDYZYsWaLDDEWKTnS1okVA1Ww+Sc0pQ6EQdrud3bt3K16Uai3dQ4ciXH99mjNnOhR/Jpd02o3f75Zv7x96aAix6WGY+voUy5al2LmzlKuugp07a2lpSbJvXweDg370q/+ajzFEse018DvyM5VfUUqXDQaDDA0NZfm9lWbdmaJ7DiPLOupBQ0MD//7v/87y5cvxeDy86U1v4k1vepNu4xed6Gr1Lc3GVTAdwWBQ7gS8evVqqqurOXDggOqyiEos3SNHovzjP3bx6qsTKPXnqcNKLOajsxM6O+GFFwS+/OU/snFjP5///H4GB6WqV9n1X8/9/2zmFABeBSZfSGazoPRYjJnpssFgUN7ESyaTsosiM+vO5XJlCbHX6zVAdJWfy7NpTJmZcKQXRlm6oI/veWxsjMcee0zuvnL11Vfz8MMP8/73v1+HGRah6IK2CljSbYJeBAIBWlpaiMfjrF69mqqqqqxQJ7WbBdNx5EiUG27o4tgxo8Q2Hz2IIjhGIFCOy5U5x+z6ryICovUlCrDPFyIYnECs/zrd7xBC7HTQilF/m1H1dO12+6SkgMysu2AwyOjoKKFQiHh8FaCn0ChX8dmKrhG1dPW2dKPRqG6V0J566ilWrVolb9xdddVV/OlPfzq/RVcLWjbSJDIXmt/vp6WlhWQyKYttLlo6Aufj1Kk4113XydGj4xRObPsQxXZEfiaZFJj5fLYgnk7iKRUMlgNLEPuBDSNasrGz7/Mg/j0nEevBzs+U33zMdAczVdadxaJ9Yyc/yi1dp3N22WhGdI2YzxXGli9fzoEDB2T//dNPP82OHTt0GRvOM9HV4l6Q4mFDoRAtLS2k02lWr149bZUyLdEI+fjWt6IcPepF7BSQeRufQH+hGkT0pQ5NeiWVSuPxaLUWXUBDnud/ilLrdj5VedQavaB/KQnl59dM4XbTfkuR9EfTM0Z39+7dvOtd72L79u3Y7XYuuugibrjhBl3GhiIVXS3uBa2iKwgChw8fxmq1snr1akUHVi//sc9nYeq+WkmyhTiOGuvnHMOIYjsw5TtmJ7q5CIguhd1AE+AHxnMe0exPzCPVLUbRna2la4R7Qa+YVwm/369rLd0777yTO++8U7fxMilK0dWCWiEcGxujpaWFaDTKpk2bqK+vV/xZrZZu7oIWRXcqpFv5zJJ9aazWBOl0G+KtfL6eWhKjiG6EmaMEUqk0s8+oFYAwYqsZ6TjYgMqzj0yiZ98ninAy6WdkJEp1tb4LVQtaLwD6i67yc9nt1u7m0jMFWMKoVj3FUMAcilR0jYzTHR0dpaWlBYfDwfr16+no6FB9VZ5NskPmBkNZmVoLw0o67QI25Dw/geijDSFaxH8BuhWPmk4LeDyzsXYiiAKq1KcubdKJF7pkElateg2HI0JtbZI1a+zs3FnKvn11vO51NdMuYCM6GmsZT39jXen5VUY0KjAxMaE46y7rW4qkE3CxpABDkYquFqYTQkEQZLF1uVxs2LBBLuBcyELmuWFjZWV6iYXUUwvEqISnVH06nRY0WrqSxapHIoWVRMJLby/09sJzz6X5whf6gS58vggNDbB5s4uPf3wFW7cqvytRS/G4F8qAfYCbY8csrFjxGj5fmEWLEqxbZ2PPnlLe8pYGGhoqp21SWSwbacXSNQKKVHS1nPT5PiMIAsPDw7S2tuLxeNi0adOk1iWFymTLJ9QVFUYEtas/2dNpAadTzW8eJ59v1hgcBIMOTp3q59SpP1Fbu5HPf/5Kw75Nq+jqb+lOdX7ZgDcAdWRuUqbTHvx+D34/nD4Nv/oVfPKTQ9jtHVRURFi6NMmWLS4uvriKSy5ZRGVlOS6Xq2g20vx+P2vWrNF1TKMoStGdLYIgMDQ0RGtrK16vly1btuD1evO+dy77pJWXG9ErTJvoKhOaBKJlq1889MyMAEeZbiNQTyTRHRiIEYnEWbkyX4v0fJ/Teyb5zq8mYD2i2Cr5QivJpI/hYR/Dw3DkCDz8MEA3bvcpqqujLF+eYssWJ299a5jt2+sVteKZceame+H8QRAE+vv7aWtro7S0lK1bt85Yc1UqZC4IAp/97DA/+EEXbvcYF17o5eKLq7niiqXU13smfSYeV1cIJleo/X4/vb3tSH5N/VAvuso2j6KIKbyG9KXJQwAx6qKzQN8nEomk+fCHx3jiiVGSyTQ2W4TKyjirVtnYts3LpZfW8MY3LppUuN1YS3cFsBcxBlqPL7ITjZbT01NOTw/8+c/wrW/FsFhO4/WG2bq1n3vu2TQp605plMP53KoHilR01d7eCYLAwMAA4XCY4eFhtm3bhsejrFGfzWbn3nuDfCDGtQkAACAASURBVO97rxIOS77JMv7yF/jxj8f56EdHcTgi1NUlWbfOyd69FezZ42blSnV/kyS6gUCA5uZmUqkUF1zQiP4ips3SnRk3sBhx0eeGsukZVxxBzGBrRlmMrz4baYIgcOedo9x/v5Vkclh+PpXyMDzsYXgYDh6Eb31rBBjE7Q5TX59mwwaxcHs6XYLoZ9Xr7iWF6KcX/baFQBCiBIOHOXZsgvXr3ykXBpKy7kCscZtZj8Ltdk/6/Y3IciuW9utQpKKrFEEQ6Ovro729ncrKSsrLy1mzZo2iaARxkQ1z331DM5QvFDd4enqgpwd+//sYEMNiiVFe3sXKlVYuusjHvn21vPWtDXi9UxVESXHmzBmsVitr1qyhsrKSRCJNvmSF2aHN0j127BgWSx2CMJNoWBBD1HLD1M6lCIv/lR5KF18CeA0x8sLIgjuT+cEP/Nx2Wx9+v1IftY1o9Fxlt9/+VnK7DCJeNATE36cMqGFyDPZMCEAt8DYKk6kYQwwvFC90TmcJbrcbt9udt9ddMBgkEAjQ19dHNBrFZrNldX4wIqJkYmJi2oSl+URRiu5MByydTtPX10dHRwdVVVU0NTXhcrk4duzYjP5ZQRD41KeGuP/+vlnVihUEF+PjLo4cEX1l3/3uENCP2x1h8eI0mza5+Ku/quKNb6xEEAYZHx+noaEhazPA4TBiI039IU+nBZYsWYLFkprFbbINMWY48w4jgZicIUU4SCnCmSKUQlzsJ9AnCkI5f/xjhA9/uJfu7qBOI+YmuaQQ064jZx8O4AIF4ySBKrLvJIwgDbQgunHOrYWpki0ya9xmkkwms7oFR6NRXnrpJbnXXaaLQqvbwRTdOSKzKHdNTY0sthLT1V8QBIE77hjigQdmJ7bTI7auaWuDtrY0TzzxKv/936c4ffqjlJaW6p6lM9UctFBTU4PFovdmlQPRJZHbwdWPuEE2AhxAjC9WxuDgACdOnJAXM6hPaGhrS3Dddb1na14Ugn5EYVuKMtF1nH1ICIhCnFv5bTZ3BAPAIcQolGzUphXb7XbKy8spLy9HEATGx8fZtWtXVmGg3F53mS4Kj8czo6EVi8UUuwznmgUhupntZmpra9m5c2febsD5IhFSqTR33DHEV7/aTzRqlNhmzRaxfOFxIEgiYaO6ulruXJCL1ap3jKcW94L4X53vCKeh7OzDjhrBBaivr2fFihUEg0HGxsaYmJggFApx+PBheSGXlpbm7TQbCKTYv3+A3/xmBEEoRBGefuAw54RN63K0MFmIQTzXRhBdVAmmz1CUCJ2dU9eU7/B4ZpfhJvlzp+t1l1kyMxKJYLVa5ULy0nGUet3NpzRxJRSl6EpXvcxGirntZvKRK7pf/vI4d97ZSSRSiNvWNOKJ/CrizruI1MbFZrPltcL1F10rWne5dd77UIA2lc+0cv1+Pz09PaxevVpeyN3d3YRCIQRBkK0qn8/HZZcJtLUlEYVJshiNWNB+RGHLTcHW+we2Ivp+a3OeH0cU4zDieelAdHv8BdFvPr2FPBvRnSlcLLMnWu7npELyIyMjdHZ2Eo/HOXPmDH/4wx9IJpO89NJLbN68eVKsvVrGx8fZv38/x48fx2Kx8J3vfIe9e/fOasxMilJ0ATo7O+nq6mLRokUziq1Eruh++9tJIpE6sm/NpA0evZRO4JzY+ie9KmWh2Ww2otHJGzU2mxGdZG1oKY5TeNHVB4vFkteqytz4mZiYYGysHLFIu4R0254bjaH1tj2GeIdzhvxiXqjlWHH2kckhxLnNTEmJ9vZJWpMtbDYbpaWlcqaoxJo1aygrK+Pw4cN85zvf4cSJE1x//fWzakp500038Za3vIWf/vSnxONxXetwQxGLrsvlYvfu3aqCrO12O7HYOatWLCgz1a2ZtNs+mypeXYgn8tS+QenOKDMN+CtfGeXee7uJRIaIxzehvwWkXnTFHWedpzEjxn5h5sZPfX09Nltu3eLMcyPT8krjdMaIx9sQzwup8NBUF/4UotAeZ/pNr7lcjsrPB69Xu+jqHaNbVVXFnj17aGho4Bvf+Masx5uYmOC5557joYceAkQXSD5X5WwoWtFdvHix6maODodDjicEKC2dblHn221PY7EkEIQWxMVjQ1yMuT/jua4LShAEAavVyqOPJnnggeOMjUXOvlKm6PPqUX/Sh8Pxs628C+k/K6zKK3cNWonHPcCmjOfSiMd7DPG2XQoLG0N0JSiJgDCmhY0ylIuuz6e9Q8N8z0Zra2ujtraWD37wgxw9epSmpibuu+++KTNWtVCkN4zayI1eUJ9ma0UQXIiL7UJgC2I2kAcxI2sQ+C3wHEoFF+B//7efrVv7+PSngxmCK2GEyKlf3KFQHIPaWunKdLvcgiBwzz1j7Nr1Mldd9WfuueckR4+Oyhsxs9uPsQLVwBpgK+L5sRF4AWWCC3NrAykPOyst1S66RhUw1ysxIplMcujQIT7ykY9w+PBhvF4vd999ty5jSxStpatHeUd9CsrYOLdZMQA8rXqEf/mXk0xt1RohuuoPu2jpGjCVadFS2CjfcxZ+9rMgH/9479mLmoO//MXBU08l+O//7sJiaaa8PEogsBH9l4Sau7HiEN2yMu2hjUZYunpWGFu6dClLly5l9+7dALzrXe8yRXc25IpuVZXeKqJ1V7ewQf9aLN1IJIHNVlw3Rj//+QDj4yE+//k0PT0tU75PSmTR352hdsOtOHy6sxVdIyxdvRIjFi1axLJlyzh16hTr16/n6aefZtOmTTN/UAXntehWV+u9yLQ63KezMuaHeyESSWC16tNtVTnajs8vfhHkppt6GR+XXDVRRD+rFKvqArxM/h30Ph/URsAUh6VbWak9CSGZTM66Slkufr+fsjL99j++8pWv8L73vY94PE5jYyPf/e53dRsbilh09XAv1NXp7aTUejJNl5Qxf0TXZnMz3zfSfvCDCb72tVyrNrddPIhWqB9RkAXEY1eBvpuXakVXXzFSh3LRrajQ3rsplUrpnjk2MTHB8uXLdRtv27ZtvPzyy7qNl0vRiq4WcoW6vn6+uBemu7WbH6IbiyU454oTEGsmjCPWAKjEmD1Z9aIbCCgVDxvnMt8kxhGzxKR4aTdiJa8atEUWFJPoKncvVFXNztI1omvE1q1bdR3TSIpWdPWoUrRokd63k1bEhaO2AIkbsU15ZnJGAtHXOz9ENxJJYrVKzSXHERepCzFtdByxbkCh3Q9GkGsVxxDTtsNn/78CWK1wrIXpXqiq0h4+ZcRGWjHV0oUiFt3ZIJWWW7LEiBgoLaIbRxRCG9kLPom2tuozof7v/sUvxujqkjL3cnEwXyxdY7AiRqa8ihguaJToFoelW109O9E1YiPNFN15jNVqldtK19UZIRRO1LermSp6QWqzrjfqx3z44V5g1TTvMMIinw+i24eY3DBx9t9q5lRMoqvcUKir0y66yWTSkOSIYinrCEUsulrdC9Jmms1mw2KxYLHo3UpFy8IpRHWzTLRYGoUtHD73TCCKbV/O82p+O7W/WXGIrsdT+NoL0+H3+03Rnc9IoutwOOjr68NisSnohqAGLSdkofqKSRghugvF0o0i1kiYqh2QmrsjJb9JBWLm2hBiFqOP7NTzQqHcvXDkyBG5MpvP56OkpERx+x0jRDccDhdNLV0oYtHVaunabDb6+voYHBykuroah6OCmK65CVqslYUguvOFUsRmnnHECIsQ+etj5JICTiN2qJjueKgR3el+Mxtif7NaRHFeefb5QUT3VBzxolPCuQgRIy9Cys5Bm83Cpk2b5OLjnZ2dcj2TzHq3Pp9Prnebi56teqQUbr17rhlJ0YouoKqAsdQJeGhoiMrKSrmrhNM5SCymp5W2UC3dmfyT88nSldriSGUAU8AooiUpxeW6OWdRdgFHUFYjQc3inuo3m65VugUxcSPTbxpEnH/k7Jiz6a82FcrOQavVOmWZTKne7djYGF1dXcTj8ayWPFJ/NCPQu+eakRS16CpB6gTc2tpKZWUlDQ0NlJeXy2183G4IBGYYRBVaLF0jIhSmQ4vo2hAXu1GFvfOhZSHl+0xmfYxMgsCfEWvJKmU2orsMeN3ZMdT+hrnNPqX+apmZdiWIRXfKUffbKT//7Pb8f7/Vas0qHi+R2ZKnq6uLcDjMwYMHFVvFM87cgLhfo1mwoisIAoODg7S2tlJeXs727dtxu910dHRkZaW53XqXK9Ri6RaD6DqBRZxrsR4DWjnXTLK4WqaI+FC/BLSIrg+4jOyavHpgPTt2Jn7E8LZmxG7BSlB+/qltlpppFafTaUKhEE1NTUQiEQKBAOPj43R3dxOLxbDb7VlCrKRRpd4pwIWgqEU3n3tBEASGhoZobW2ltLSUbdu2ZTnZc8s7er1635YUg6Wr5bBLc8xssX5hxutGuEjm4y2jWtG9FFhC4S5K3YiWexzloqv82Dkc2iVD2kSbqmtwIpHI26gy1yp2uVyyVVxsMbpQ5KKbiSAIDA8P09LSgs/nY+vWrZP6LIFYyDwSOVezVuweoSdaLN1Cb1IZsZFmRLhTIURXrRgqFd0o4vJarOE7tOBHFFspxE2Nr1e56KrtBJzJTNloDoeDysrKrPCv3JZKPT09slV87Ngx2traZH9yvvWuZY47duygoaGBX/3qV7MeLx9FLbqSpTs6OkpzczMej2dKsZXIbQA5ffcILWgRn4UgukZQrKIrIP5eJejfcy+XBGLExamc7zDGp+t2a/efagkXy22pJJFIJEgkEhw/fpy2tjYuu+wyotEoP/zhD9m4caPmOd53331s3LgRv39yP0O9KGrRHR8f57XXXsPlcrFlyxZFLTUcDocBhcwzOV8t3WJFregq+e2kCIRMpJ57mU0uZ+uS6UBM4MjtNgLqjrHyebjd2u9o9MxGczgc7N69m/7+fhYtWsSdd95JKjW7c7S7u5snnniC22+/nS9+8Yu6zDMfRS26AJs2bVLVcnly94j5YOkaZQVNRSEqZulBsVq6+cjXc0/alOxBdA9Yz74+04V7HHgZMaFCj3kWbzZapk93tmN/7GMf43Of+xwBfcOZJlE8EcV5qKysVN3jfnIh8/lg6RZ659+0dM9RKNHNh7QpuQpxU/ICxB5rPkRreIJzjS45+9wrwG+YXnBB3TFW0wl4dpauEWUd9UgB/tWvfkVdXR1NTU06zGp6it7SVYvxortQLd2F6tNVSyFiQqvOPjIJAz9BrPerBGPcC7Npv25UWcfNmzfPepwXXniBxx9/nF//+tdEo1H8fj/vf//7efjhh3WYZTZFbelqCaa2Wq1ZYWb6VxpbqJau6V4QmaslU4K6C58acStc+3Ujit3o0Qn4f/7nf+ju7qa9vZ1HHnmEffv2GSK4UOSiqweLFs0H0S00WqwN070gMpdLRs0xMMbSLS3V3pTSqLKOZpxuAdEj33r+tOwRKNzttJa/eaFaumqZy5TTubd0y8sXbidgiUsvvZRLL71U1zEzOS8tXYvFQjotikhDw3xpTpkv7McotFxrF6roGhEyZhRGia5yS3c27dfNAuYi56XoZm6m6R8yptW9EJ35LbpRLD5dLag9nsXkXlCTLq7m4q+mE7D2urVGWLrBYFB1BNNcU9SiO9vuEdIY+laFs6PNQjNFdzLz0dItFp+uGtFVLuaVlfNHdIuxli4UueiCNuHNDRvTvzKcFmu3kO4FU3TPUUzuBTWWrjHuhdl0AjbCvQDFVUsXFoDoaiG30pj+54EW0TUt3bmhWEQ3jbq5qjkHlYuuz2fTXIhcb0s3nU4XneBCkUcvgLruERKZ9RfS6TQOh0A0qufB07KZpmvPoBnQcuLPRb3c+big5kp0jWxyqdyC9vsHOHhwKKv2bWlpKV6vd8bbfEEQdHUFBAIBSktLZ37jPKPoRVcLkqXb29tLW1sbTmcj+i7w+W7pFkv0QiEoFktXregaY+m+/vU7cDjsWbVvpY4QgiBQUlJCaWlpVkcIoxgfHy+6GF1YAKKr9vZCEATC4TCDg4PU19ezY8cOfD4/IyN6isp8b8Nuxumeo1hEV22he2NEVypiPlXt23A4TCAQYGRkhI6ODhKJBC6XC5/PRyKRkDv36uEWKMbECFgAoquGkZERmpubsVgsLFq0iA0bNgBGdI/QcnUvpHtBy2E33Qsic7UNYqSlq0zQZ9LJfH3SBEEgFosRCATo6+ujpaWFSCSCzWbL6gbh8/lU+3snJiZ0SQEuNOeF6E5MTHD69GkcDgebN28mGo0yMjIiv65/94hisHTV9oYrFkvX6DjdYnEvqKmRoFR0Lezf/zIXX1zF2962hOrqmRMlLBYLbrcbh8OBx+PhggsuEL8xmSQUCsliHAwGs1rzSC6K6RpWmpbuHDHdbUogEKC5uZl0Os369evlBnapVCoreqGsbD7UX5iLNuxqblmLsfGkEkzRVXrupdMWHn3UwaOPBrjxxtdwOiPU1SXZsMHB3r0VXHHFYjZuzC+CueFidrud8vLyLEtVcv3la+Oe6ScuKSnBarXqVuym0BS96OYjHA7T3NxMNBpl7dq1k9IE52chc1N0JzMffbpz5V5Q69M1okda5vGwEo976e6G7m546qk4n/50B1brKSorYzQ22mhq8nH55XVcemmdonAxi8WStzVPPB4nEAgQDAYZGRkhHA5z//33Mzo6yrJly/jjH//I1q1bNQtwV1cX1113HQMDA1gsFm644QZuuukmTWMpoehFN9PSjUajtLS0EAgEWLNmDdXV1Xkt4VzRraw8Xy1dNZiiKzJXfua5dy8oueCk025GRtyMjMDBg/Dgg4NAL15vmLq6GDt2RLnkEuXuCRDbuFdXV1NdXS0/97WvfY277roLgB//+Mfcdddd/O53v9OcLPWFL3yB7du3EwgEaGpq4vLLL2fTpk2qx1L0fYaMWmDi8ThtbW2MjIzQ2NjIpk2bpv3x52ch80K3YTdFV6RY3CZqRVdNYRqlF3yt68ROKFRGWxu0tcFPfiK6J0pK+vjKV5Zy9dVbVY9YWlqKzWbjqquu4rLLLtM4L5HFixezePFiedyNGzfS09Njiu5UhMNhDh48yMqVK1m3bp2iK53NZpOrjAHU1MyH6AVTdE2mw6jkCDV3WHr5sxPAq4TDpwmF3ql5FCM20trb2zl8+DC7d+/WddxMil50S0pK2Lt376wyXfSvqVsMoqv20C9U0S2Wv0vt+aHUkFAzrh6i2wscROr7VlenvUKY3mUdg8Egf/d3f8eXv/xledPdCIpedC0Wy6xTC/Vv2aPFvVAMbdjnArWhbWopFtE16vwolKUbBQ4hto0/R3299jRePaMXEokEf/d3f8f73vc+rrrqKl3GnIoFIbpaEQQBi8XCkiXzoZD5fBfduRIno0W3WFBjkapZE4UQ3VbgMPli0Rct0i66gUBAF9EVBIF/+Id/YOPGjXz84x+f9XgzsSCqjM22vOOSJfPBvVAMHYEXIsUi6GouymrWgxox9yGeN0HE1vD+GT4fAJ4BXmSq5J/ZiG46ndalatkLL7zAD37wA5555hm2bdvGtm3b+PWvfz3rcaei6C1drUii63A48HpN98LMzKWlayRq/q65TEueD6LrBBrOPiRSwAiiCEup7G5E6/Y4M81bq2hqLS+Zj7/+67/WdbyZWBCiq6W8Y27YmNUKad2MzYXahn0umE+iO5eoEV01RoQa90K+89oG1J19SPQAR2cczWqdnWsQiq+AOSwQ94IWjO0eocXSLbR7oViut6boiswHn67ShAtldUTsdu3yEwwGi7KWLpzHoutwOLLqLzi0NvHNSzFYugv10BtZ8KZY3Atqjq0aMddbdLVbOhMTE4aGdRnJglh5evRJczrnunNEoTHLOxYXakRXjZjNnaXrcs1OdIux2A0sENHVQq7out16Lm4r81/UtJzwha4PUQiKxb0wH3y6SlOLlYmu263dxVWsXSNggYjubC1dQRBwufSOHtBi7c73jsBzIbrzyac7l1a3UZljasZV2n5dmeh6PNpb+RRrLV0ont0U3ZFEd3R0lDNnzuB0LkXfn8OJehGNAiU6zmE6tIhuoVOVwRRdifngXtBXdEtKzk/RXRCWrhbi8Ti9vb10dHSwZcsWamqUnlBK0WLpzvc27AvRvVAsqBFdNcaDEaKrbEyvV7uRU8yiuyAsXTXuhUgkQnNzM8FgEK/Xy0UXXQRAebne7XK0XMUL6V7QcuhNS3fuWHjuBUjw0ksv4Xa7s1r0uN3uGde03+9n5cqVCr9nfrEgRFcJ8Xic1tZWxsbGWLNmDT6fj5MnT8qv6989Yr63Yddyk3O+i+5cYpSlO3eiu3RpHU1NTcTjcYLBoNwvLRqN4nA4ZCEuLS2lpKQkS4hNS3eOme6qmEql6OjooK+vj5UrV7J+/XosFgvJZDIreqGqaj6kAs/3jsAL0b1QLJbufHAvKFsjTmeauALdrahwk06nsdvtVFRUUFlZicVikdenJMTDw8NEIhEsFgulpaX8/ve/Z3BwEI9HH5fgb37zG2666SZSqRT79+/n1ltv1WXcqVgQopuPdDot+2yXLFnCnj17svK8bTYbqdS5E1l/0Z3vbdi1+HQLXR8C5pelWyzuBTUXfP0vpPG4srnW1pbhdDoRBIF0Oi0/AFlgy8rKZCFOp9OyEJ86dYp/+Zd/wW638/a3v53/+q//0jTXVCrFRz/6UZ588kmWLl3Kzp07ufLKKw3rGgELRHQzLV1BEBgaGqK5uZmamhp27dqFI0+6Wa51XFt7vlm6ZvSCSLGI7ly7F9Ssj5ndZBYLrF9fJ9fCzjSI0um0LMTSfyW8Xi8f+9jHePLJJ3n88ccpLy9naGhIxdyyeemll1izZg2NjY0AvOc97+Gxxx4zRVcpY2NjnD59Gq/Xy/bt23G7lfeJqq+fDz5dU3QnY2akiagRXSMsXTXHYXrfgtvt4Wtfu5prrrkg7+tTCTGIzWe/+MUv0tXVhcvlwul00tDQkHccJfT09LBs2TL530uXLuXFF1/UPJ4SFoToCoLA0aNHSafTbN68GZ9PeQsQqZB5ff18KGSudwTFdBSL6KrFrL1gjOiqsXSnNh4uvXQLP/3ptaoTI6xWK0eOHOGmm27iyiuvpK2tLe8dbDGwIETXYrGwYcMGXC41bafP+XXtdvs8KWReyI2qhSq6aikW0VVb91bvcdWcL5NF1+v18p3vXMsVV6xXMc7Z0WIxPve5z/Hss8/y7W9/m61b1XcPnoqGhga6urrkf3d3d8/KclbCgkmO0LKTaWz3CC1X4UKKrpbr7ULcSFNDsVi6akRX6TmnVHTTOWNaeNvbttPT80lNgnv48GEuv/xySktLee6553QVXICdO3dy5swZ2traiMfjPPLII1x55ZW6fkcuC8LS1Uqm6Doc55ula8bpihSLpWuUe0HZMXW7LVRVjTI46CKZ9Coaz+st4XOfu5TLLltDKCTWv1XaKSIWi3H33Xfz/PPP89BDD7FlyxZFn1OL3W7ngQce4M1vfjOpVIoPfehDbN682ZDvkr/T0NELiB7lHfXtHqHF0i2kqJmWrshCFF01bjZlF/rlyz385CdWxsYG8XpX8dRTcZ57LsSJE2n6+13E4yWIF/IEYOHqq3fxzW++g0gkTCAQoLe3l2AwSDqdzkp6KC0tneSbfeWVV/jYxz7G1VdfzR/+8AfsdmNl6q1vfStvfetbDf2OTBaM6GrBbrdnFTK321EU1K0MLZZuIUW3WOJ0jaZYRNcIn66geNx0Oo7D4WDHjh1YLBY2bYJ//ddzrw8MxPjlL0f505/C/NM//Su7di0VZ+Isz6p7m06nCYVCBAIBBgcHaWlpIZlM0tzczMmTJxkcHKS1tZXvf//7hlucc8V5LboOhyPL0nU4LMTjeqWFqhddhyOOyzVBMOjF+ENTLBtpaoXOiOgFqQ38XImugLp2TkotXeXHs7q6nBUrVkz5en29i/37F7N//+Jpx7FarbKFu2TJEkCMIBodHeXQoUNy+u973/teHnjgAV7/+tcrnmOxsGBEVw/3gssFoZBeM1LvXqipsdDcvJpIJMkTT4zx29/6efHFKN3dLmKxUvRtJlkslu5cuhfKgDciHssAMI7Y4bYMsRGj8jjw2aH2d1cqusr3EEpLjflbI5EIn/3sZzl48CDf/OY32bhxI8CkpIiFxIKJXtCCsd0j1ItuPC4uLo/HzuWXO7nhhlF+/GMr/f3LueUWvevsFovozhUXA1cgCpgVKAdWIAouQD9wErHr7XGgE+MKFqm9w1AqkMrH9fnUhWMq4cUXX+RNb3oTS5Ys4ZlnnpEFF0QjSmt79vnOeW/phjJM25ISPUVXvXshkUgRjUZpbm4mHo+zceNGOdGjpiaFvhlrxSK6hbZ0VwJ7p3gtEyuQm4TTjyi8NYjHX3rM1raZe0vXYkkyNjZGaWnprDe2IpEIn/nMZzh06BA//OEPWb9efShZMbNgRFcLuZauzzfXlm6SI0eOsHr1ampqarIuJPo3PtUiunNxu1co0fUAlwPThUPNhBWx80f47EPCTrYIqxVitaKr7ILvclmJKbyOl5W5GRgYoLm5mVQqhdfrlQvS5ItAmIo///nP3HzzzXzgAx/g3nvvXbDW7HQsGNHVYulmtmEPh8NYLGG0RR3kHZ1zGzDKSKXS7Nq1S849z6SsTG9PULFYuoVgF7AG42rrJs8+JCEWEO9a1qn4vBqUrYVYTHmoTkNDDRs2bADECIRwOIzf72doaIiWlhZSqRQlJSVZQux0nltL4XCYu+66i2PHjvHII4+wbp3Sv33hsWBEVwt2u514PM6pU6cYHR2ltrZR529woKaeQjotTHnxKC/X2+Iz43RFMXs7ai+O2rEg+n6fBy5T8TmjfndlYu7xuLj44jXyv61WKz6fL6vGiSAIshCPjIzQ3t5OPB7noYcewmKx8MILL/DBD36Qp59++ry0bjM5b0U3nU7T19fH2NgYixYtYu3avEeX/wAAIABJREFUtdTW+tHTb+pweEgk1InugQMHcLvdlJWVUVZWRnl5OU6nk4oKvS1dK+rFZqHsJguA/+yjUGFgceApYOLsv9UIjxrRVfP3zCy6F17YyGOPXUdt7fRuF4vFgtfrxev1snixGDYWDAZxu92cOXOGN77xjTz33HM8/fTT/P73v1cxx4XHghFdpe4FQRDkoOy6ujpKSkpYvHgxqVSKykp9F2Aioc5VIQiwZ88eotEofr+f8fFxOjs7icfj+P0+YPoYSPXYUHPrarXGSaeTFPa0MSJON4EovC5EMTTyYmJBjG44lvO8UT5dfUTX6XRx773v5B/+oUnFeCKCIPD8889zyy23sH//fh588MHz3rrNZMGILojCKwhTW27j4+OcPn2akpIStm/fjsPhoLe3lxMnTpz1QykvCakMbf5hj8eDx+Ohvr4eEE/igYEg+tfbVSe6O3c6eeKJGn79azGG+KWXYnR2Og2IIc7ECEtU2sySSCKKb+zsf+Po427wA0+S38VkVPPI2Yvunj3r+fnP3095ufrY3GAwyH/9139x+vRpfvazn7F69WrVYyx0FpToTkU4HOb06dOkUik2btyI1+uVW4Ps3r2bUCjExMQEMIoYj6kXamIbLbzpTZvl+r5Zr1gs1Nf7MEZ0lZNMpvB47FxxRRnr1w+QSCRYt24FyaSTxx8f4fbbYWRE71OqELf/9rMPKRZaSo+VBLgP8ViqiUh5AdF/OxXzwb2QHTImFRd/97vzFxefDkEQ+OMf/8itt97KDTfcwFe/+tW8G8ImC0x0cy3dRCJBS0sLY2NjrF27lurqalKpFKlUCovFgtVqxWq1Ul4u5odv3x5DzDzSC2WWbmPjEh5++D1ceOHU7gMt0Rkzo050E4kU7e3t9Pf3Twpr+8AHFvHww+M8//xCqLlrQRRYB2IIWSWiC2IE8cIcO/ueErKFOA78BTih4DuMcS+4XFb+6q9GOXkSBgfdpNPTJdWcO1bbty/jc5/bQ22th8HBQUpLSxW1QgcIBALccccdtLa28n//93+GtEaPRqNcfPHFxGIxkskk73rXu7jzzjuz3vPQQw9x8803y/Vwb7zxRvbv36/7XGbLghJdiXQ6TWdnJz09PaxcuZJ169YhCIIckys1ustl0aLClncsLS3lS196B9dee6Gi0SwW0e+rH+pE1+8PkU6n2blzZ14fnd1uxIVhvtTTtQK1Zx8SKUQRHkUU5IMoz0ozxtItKbHz0EPVnDlzhqVLlxIMenjssTGefz7MyZMwNOQmlZKEOCEXF3/b29YRi8Xw+/0EAgF6enqIRqO4XC45BKysrCxLiAVB4A9/+AO33XYbH/nIR/j6179umHXrcrl45pln8Pl8JBIJ/vqv/5q/+Zu/Yc+ePVnve/e7380DDzxgyBz0YkGJriAI9Pf309raSn19Pbt378Zischdf6cSW4nNm63cdluU554L85e/WBgdLUEQZpP+mF90bTYHH/7w6/nc596iyoK1WiGla/SQusNvtzvlBn75X5/tfPIxX0Q3HzbOCfER1KUBqxEn5XcPFkuarq4utm3bJvcI3LDByy23nHvPqVNh/u//RhkeXsRnP/tJHA7xwLndbtxuN3V1dfJ7o9EogUAAv99Pb28v0WiUI0eOcPz4cQYGBggGgzz22GOGWLfZf5dFDlFLJBIkEgmD7v6MZ0GJbnNzM9FoVN4kk7qJziS2Ei6XjU9+cqn8b0EQeO65CR57bJwDB2K0tNhVVgDLFV0Ll166mYcfvobKSvWdLvQXXXWWbjo9vZltcNnTeY7aCAhjLF2Xy86FF1447fm+fn3J2VoeS6d8j4QkxLW1ooUvbuoO8POf/5yamhrKy8t5xzvewRe/+EUuu0xN7LF6UqkUTU1NNDc389GPfpTdu3dPes/PfvYznnvuOdatW8eXvvSlrKaT84UFtUzWrFlDKpWSN8mUiu1UWCwWLrmkgksuqZCfi0SS/PKXY/zmN34OHozT1eUikfCR33I5J7pLl9Zy1127Wb7cSWvrSTkGV7plm4nx8XGs1vQU36MNm82uSsTD4QjHjx+X5+7z+bLcDAvbvTATRoquckvX7XYZZgH6/X4++clP0tPTw49+9COWL18uv1aIimA2m40jR44wPj7OO9/5To4fP57VUeLtb3871157LS6Xi2984xtcf/31PPPMM4bPSy0LSnRvv/12fD4fO3bsoKmpidLSUt2/w+Ox87d/W87WrSMEAkHWrl1MNOrh5z8f4emnQ7z6qkB/v+Q3c1Ba6uMLX/hb3ve+c35byXfm9/vp7u4mFovh8XhkES4rK5Nz2aPRKGfOnCGZTOJwLFOcK6+EVEptI08Hy5cvx+/309PTQyAgbjqWlpaeLVSt3npfOKgVHaM20vQP3RMEgWeeeYbbb7+dm266iQ9+8IOTfLeFjFSoqKjgDW94A7/5zW+yRLe6ulr+//379/Mf//EfBZuTGhaU6O7fv58DBw7wxBNP8OlPf5p4PM6WLVtoampi586dbN68eVZtm9Np0V/W29vLypUrWb9+vWxV/PM/L+Gf//nce0+cCHHw4Fquv/7ySZaHy+WitrY265YtEong9/sZHh6mtbVV3vRLJBIsX76cZcuW4XCMo2+6qrrDn0ql5YvCuedSss8vFguhv/Cqs9re+U6BkpIJXnklSUeHg0jEi3ExxJnMD/eCxSIwMTFBaWmpLkI4MTHBJz7xCQYHB3niiSfm7HZ9aGgIh8NBRUUFkUiEJ598klsyHdVAX1+fnA33+OOPZ5WKnE8sKNFdv34969ev5/rrrweQnf4HDhzgq1/9KidOnKCkpISmpiZ27NjBjh07WLFixYwnpyAIDA8Py1lsu3btmjHDZvNmL5s3r1I0b4vFQklJCSUlJdTX18stS6qrq/F4PAQCAV5++WXEeq56HjJ1YrR375pJz9lsNioqKqioqKCuLoD+scTqRHfjRi+f+EQjvb29dHZ2Ulu7gj//WeDJJ4McOZKiu9tJLOZTPe7MGCe6ZWUu/H5l73U6bXR3dxMMBuXNJ+lC6fP5FAuxIAg8+eST3HHHHXz84x/nuuuu092aVRIGFovFuO666/jTn/7E6Ogoy5Ytw263c80113DFFVdwxx13sGPHDq688kruv/9+Hn/8cex2O1VVVTz00EO6zlcvLNNlcFGYKiAFQ2oLcvDgQQ4cOMDBgwfp6Ohg6dKl7Ny5UxbjyspK2Tr1+/2cOXMGt9vN6tWrFflftRIIBDh9+jQej4fVq1fjcmXf/q9bN0JPj56H5AnEFNXpqago51vfuoa3vnX6ylAf/WiAhx7SW3R/CHQpfvfNN1/CFVcsorS0lNWrV+et/VpTM0wkouMUAbGIzQsq3v+PQJXC9x4A/qDonRdf3Mj/+38fAcS7kGAwKLuycoW4vLwcr9c7SUzHx8e57bbbGB0d5cEHH5TjXvVGEARCoVBWGNh9992XFQb2ta99jWPHjvHggw/yyCOP8Itf/IIf//jHhsxHZ6a8qi8oS3cmLBYL1dXVvOUtb+Etb3kLILoM2tvbefHFF3n22We59957CQQCrFq1ivHxcVasWMFdd90luwKMIB6P09LSQigUYt26dVm375m4XHpXw5r+8Futdj7ykUu45543K9qcmYXnZhrUWaTDw8OsW3fxlL+hccyPjbTGxnPhXjabTU78kch0B3V0dBAMBrFarYRCIY4ePYrb7ea73/0uN998M+9///sN9dUqCQN77LHH+NSnPgXAu971Lm688ca8WZvFxHkluvmwWq00NjbS2NjItddeC4hX1/vvv59LLrmEaDTKO9/5Tux2O9u3b2f79u3s2LGDtWvXzrqIRzqdpru7m56eHlatWsWGDRumPZne8Q74/vfHGR31zDJ+WGLqw79z51p+/OP3nk0/VobDMffRCw0NS+ZAcGGuN9KsVju33vpmbr/90mnfl+kOkkdPpThy5Ah//OMfaWlpwe12841vfAOn08l73vMeFfNUz0xhYD09PbIf2W63U15ezsjICDU1NYbOy0jOe9HNx+te9zo+9KEPya4EQRBkv+qLL77Ipz/9aZqbm6mrq8vyD9fX1yu+Ao+MjNDc3ExNTY0iHzHAZz5TxWc+U4UgCDz77DiPPz7OgQNxWlvVxg9LTP7OqqoKvvOdd3P55ZP9tzMxHyxdJe/XN6tPHlXl+/WzdNetW8Yvf3k9S5eqrxsiCAK//e1vufPOO7nlllt473vfi9VqJRAIEA6HZx5glswUBrYQMUU3D9u2bcv6t8VioaysjH379rFv3z5APFl7e3t58cUXefHFF/n617/O8PAwa9eulUPWtm/fTklJSZYQh8Nhzpw5g8ViYevWrXg86nf7LRYLb3hDJW94Q6X8XGb88CuvJOnqchKLeZneojq38K1WO1dfvZH/+I9dVFT4CIfDeDwexRcR0X84xlyHjQWDAUKh0KTf3XgKH71gtzu4664ruOmm16n8bpHR0VFuueUWIpEIv/vd7+Sdf0Buk14opgoDa2hooKuri6VLl5JMJpmYmMgKDStGTNHViMVioaGhgauuuoqrrroKEIXntdde48UXX+QXv/gF//mf/0kqlWLr1q1ccMEFvPzyyzQ1NfHud7+bqiqlmyjK8HjsXHNNLddcU0s6naanp4ejR89w/HgNL71k5dVXBQYGMvPuQVr4e/as55FHrqWiwilvuvT39xOJRHC5XFnxw7mbe4IgMDQ0RGtrK273Cl3/JgCv10NG79AZSSSSNDc3Ew6HcTqdWUkoLpfrbFEk3adJod0LW7eu4vHHr5+xuHg+BEGQwyo/8YlP8J73vGdOfKRKwsCuvPJKvve977F3715++tOfsm/fvqL258J5Fr1QaKQWJnfffTff/va32bBhA8PDw5SXl8uxwzt27KChoUG3DYuxsTFOnz5NdXU1q1atmuS2OHEixM9/PsJzz0Xx+7v4n/9Zxb59U9dTkAqqT0xM4Pf7icfjlJSUUF5ejsvlore3F5fLxdq1a7nvvhR33qn3Len/AacUv/uTn3wTt912OXAuCUWaeywWw+12s2/fUuJxvRfubxHrLyjD7f4w0ajSOsS/RGz3Dg6Hk0996nL+6Z/2yBcRNYyMjHDzzTeTTCb56le/KtdsnguOHTvG9ddfL2eRXnPNNdxxxx1ZYWDRaJQPfOADHD58mKqqKh555JFp63/MI8zohblAir9dtGgRx48fp6qqSo75ldwS3//+9+VqaJJvePv27ZSXl6taUJmZaxdccAElJflL+onxw5J1NHNzwNwiKJJ/u7W1lYmJCbm5p2hdVqJfY08JdaLictkz/v//t3fmQU2eaxu/3gQEZBHcWBSrJWXHsKunVdE51MooUsGi7VesFi3VFEbtOn5treOIa0VL61Y+UHuUtmKFsYDHSpUejgRlwB7FI4IsAgEFZQkKhrzP9wfNa0IICZiF5f3NMCPJY/KEwPXeuZ/7vm7lJpSOjg4AYm1u8C/6F+k2NjqhsfExjh69hfx8ChUV5qitNVGREurO6QYEvISkpIUgpBMlJSXMRaTnaKfeIIQgIyMD27dvx+bNmxEZGamziPHevXuIiopCQ0MDKIrC2rVrERcXp7Dm0qVLWLJkCaZNmwYOh4OIiAh88cUXAICtW7cy60xNTfHzzz/rZJ+Ggo10BwE0TaOsrAxCoRAFBQUoLCzE48eP4eHhwQixp6en0kd72f+trq5mPG51WdoGdJdklZWVwd7eHo6OjuBwOKBpGmKxGN98I8b27doW3QwAt9Suoigu1q6dg717F6oVExubRjzVfHSdhmhW8yzjzp0NqKqqAo/HU3jPGho6kZ7+EBcvivHnnzREIlOMHn0Z+/b5KJmLyy4ispSQ/CcRKysrGBkZMVaMmzZtAkVRSExMVHAR0wUikQgikQi+vr5oa2uDn58fzp49C3d3d2aNrDzz3LlzOt2LAWEj3cEMh8OBs7MznJ2d8fbbbwPort0tLi6GUCjEkSNHcOPGDZiamsLHx4cR4j///BMSiQSBgYEqPW61xZMnT1BaWgqKohRsA2X7t7KywsSJxgD6kYDVCPXRmKurI9LS3sbUqTZq1+qO/kW6zc3N8Pf3V2pLt7U1wdq19li79tlthLzd64WEoqheRzvJpvIKhUJs374ddXV1cHNzw+uvv47Gxkadi669vT1zKGdpaQk3NzfU1tYqiO5IhhXdQcqoUaMQGBiIwMBAAN1/TM3Nzbh69Sqys7OxefNmWFlZ4cUXX0RpaSnTUSc/zUEb0DSNqqoqNDQ0MNM3VNFLIK5TTExMsXt3WL+HJxr6II2i0C8B6s/7KZvK297ejrS0NHh6euK3335jOjHr6+v1Kn6VlZUoKirq1YbxypUr4PP5cHBwwJ49e+Dh4aG3fRkSNr0wxCCEYOXKlVi1ahXmzp2Le/fuIT8/HwUFBbh69Sqam5vh4uLCHNTx+fx+lX7J8/DhQ5SWlsLW1lZjj4rLl5uRkdEyQP/h3jgH5fE3FIKDp+PUqTdgZtb/dIa1dSMkEvXr+scvAEo1WsnhUGhr26XtDQDofg/OnDmDXbt2YcuWLVi6dKnBTvvFYjHmzp2LzZs3MxU+MlpbW8HhcGBhYYHMzEzExcXhzp07BtmnjlD5Q2dFd5jR1dWFmzdvMt4SxcXFTEpA1sjh4uLSZyqio6MDpaWloGkaLi4uA6olljGw+mF5FEV37Fhr/OMfb2HOnKkD2k97ezvs7R9DKtW2EKUBKNNoJZdLobVV+6Lb0NCATZs2wczMDPv37zdo15ZEIsGiRYuwYMECbNy4Ue36qVOn4tq1a0O606wHrOiOVAghEIvFKCwshFAoxNWrV5mSMj8/P/j5+SEwMBB2dnaQSCS4fv06pFKpTg/l6us7ceZME/75zxYUF0vx8KElpFJV9abdB1QcDhdvvumLdetc8PjxYxgbGyvUD6sboijz2Hjw4AFefdUJXVqfn/kzgLsareRyKfzrX//DVBxYWlr2asyjKTRNIy0tDXv27MHWrVsRFhZm0FpW2aexsWPHIiEhodc19fX1TAdnQUEBIiIiUFVVNeRrcOVgRZflGbJZcgUFBUxEXF5ejq6uLsyfPx/Lly+Hr68vLCwsdPJHIJVKcffuXTQ3N8PV1RWWlpYK9cO3b8vPp8uEm1sLzpyJwpQpz9pcnz59qlCD29HRAVNTU0aIx4wZwxxSNTc34/bt25g4cSJeeOEF2Ng81LroGhunQyL5r0ZrR43i4t69/2X239bWBpqmmeGPskGQmtRu19fXY+PGjbC0tERCQoLOurU0KQMjhCAuLg5paWmoq6sDj8eDuXn3xXT79u2oru4eSR8TE4PExEQcPHgQRkZGMDMzw9dff42//W1gnXWDFFZ0WVSzY8cO5ObmIjY2FiKRCAUFBSgqKsLTp0/h5eXF5Ifd3d2fywQe6O5CKi8vx6RJkzB58mSVoi7LD7e1tWHx4im9rum5vmcjh0QigVQqBUVRTOTO5XJhZdWo5VlzQH/SC6amRmhqile4jaZpxv1LNpGXw+EwQiyzYZT9vGiaxk8//YR9+/Zh27ZtCA0N1WmUqEkZWGZmJr755htkZmZCKBQiLi4OQqFQZ3sa5Ixs0c3OzkZcXBykUimio6Px6aefKtyfkpKCjz76iPENFQgEiI6ONsRWDUJLSwusrKyU/mg7OjpQVFTERMM3b96EhYWFgsnPlClTNIrIOjo6cPv2baY8rreaY21y//59lJWVwc7ODiYmJoyQEULw97+/CJrWtkBpnl7g8x3x73/Hql0nlUoZEW5paUF7ezvOnz+P6upqVFVVwd7eHkePHjWIF8GSJUsgEAgQHBzM3Pbee+8hKCiIcetzcXHBpUuXFDwdRhAjt05XKpVi/fr1uHDhAmNWHhoaqlQ2ExkZicTERAPt0rDI+63KY2pqilmzZmHWrFkAuqPJpqYmxgQ+NTUV1dXVmDJlCmPy4+fnp2ACLxtxJBKJ1JacaQOZuHO5XPj7+zMdWrILanfk+0gHz6y+ZIyiuBAI5iE+/lWNHpHL5cLGxgY2Nt31xzRNo6SkBHl5eeDxeBCLxZg/fz6+++47vPzyy8+1+/6gqgxM3oYRACZPnoza2tqRKroqGfaiW1BQAB6Px/RrL1++HOnp6Wyh9gCgKArjx4/HwoULsXDhQgDdQlBRUQGhUIicnBzs2rULYrEY7u7usLOzw+XLl/Htt9/qvHmDEMJ4E/cl7rrbQ9/5iqlT7ZGRsRJOTgO76IhEIsTFxWHChAk4d+4cI8RA92vXF2KxGOHh4UhISDCQb/HQZ9iLbm9X397yTGlpacjNzYWzszP27dtnsAF8Qw0OhwMnJyc4OTnhzTffBNBtqvL+++8jOzsbPj4+WL9+PYyNjeHj48Pkh3k8ntZMfsRiMW7dugVra2uNxH3SpC5UV1PQ7sDK3iNdTc3FVT4qTePkyZNITExEfHw8QkJClNJA+jrxl0gkCA8Px1tvvaVUdws8s2GUUVNTo7NRP0OZYS+6mrB48WKsWLECJiYmOHz4MFauXImcnByV61evXo1z585h4sSJuHFDud9edoqbmZmJ0aNHIyUlBb6+vrp8CYMKU1NThIeH48cff/zLSpGgtbWVMYHfsmULysvLYWtrq5AfnjhxYr8ERCqVoqKiAo8ePWKqIDTh1i07LdQP90RZdF1cHJGRMTBzcQCoq6tDbGws7O3tkZubqzDtQd8QQvDuu+/Czc1NZd1taGgoEhMTsXz5cgiFQowZM4ZNLfTCsD9Iu3LlCrZs2YLz588DAOLju0+NP/vss17XS6VSjB07Fi0tLSofMzc3FxYWFoiKiupVdNlTXPUQQlBbWwuhUMgc1DU1NcHZ2ZnJD/v4+Kg0I5d1yzk4OMDR0VEr0V59fSfS0ppw8WI7btwgqK/v6T/cF8kA7gMAuFxjfPzxXGzY8MqAzNRpmsYPP/yAgwcPYufOnViwQLMZdQNBXQAhcwObMGECc6G0s7MDoFwGRgiBQCBAdnY2Ro8ejeTkZPj7++tk30OAkVu90NXVBWdnZ1y8eBGTJk1CQEAATp48qdDnLRKJmCvyL7/8gp07dyI/P7/Px62srMSiRYt6/UVlT3EHhlQqRUlJCeO2VlRUBEIIpk+fzkTDNjY2yMjIwMyZM+Hq6vpc3XKacO1aI5KSyvGf/5igqsocjx7J6od78n8AHmD69Gk4fnwJjI0laGlpYczU5euH+6rcqKmpQWxsLBwdHbFnzx6Vh5zaQl0AMQLcwHTFyK1eMDIyQmJiIhYsWACpVIrVq1fDw8NDwSj5wIEDyMjIgJGREcaOHYuUlJTnek72FHdgcLlceHl5wcvLC9HR0Yxjlqyb7oMPPkBpaSl8fX1RW1vL5IcnTZqk9UhQZpkplTZgxw5XRvxU+UuYmY3Czp3LezXfkTdTr6mpQWdnJ2O/KHtca2trHD9+HIcPH8bu3bsRHBysl1ztnDlzUFlZqfPnYXnGsBddAAgJCUFISIjCbfJGyfHx8UzawRBo+hFv2rRpAIClS5cyhs/DGZlj1pw5c9DQ0ABvb29kZWWhq6uLMYE/duwY6urqMG3aNAUT+N7qjjWlra0Nt27dwrhx4xAQEKBw4EdRFIKCbBAUpDifjqbXwdy8d/Od3szUnzx5wojwmjVr0NTUBEtLS6xZswbjxo0bVO2wI9UNTFeMCNHVN/09xX3nnXcgEAgQFRWlcs3s2bNH9Ee8ZcuWYdmyZcz3ixcvxuLFiwE8M4HPz89HVlYWtm3bho6ODiUTeFVTFWTIH8y5u7vDwkKz8fNmZv37M5JNFDE1NUVWVhY4HA6SkpJgZ2fHWHf6+fXPrlJX+Pr6oqqqinEDCwsLG25uYHqHFV0d0N9TXPYj3vMhbwIvu3B1dnYyJvCHDx9mTOB9fX0ZIZaNigG6O9ju3r0Le3t7+Pv76zzSrK6uhkAggLOzM/Ly8hiB9/Hx0enz9hf5WtyQkBCsW7cOjY2Nw8kNTO+wojsAVqxYgUuXLqGxsRGTJ0/GV199BclfBq0xMTEICQlBZmYmeDwec4r7vLAf8fqHiYkJZsyYwXRNyUzgCwoKIBQKcebMGVRUVMDW1hZdXV2wsbHB7t274eDgoFPBpWkaSUlJSE5Oxt69ewf9dNuebmA0TQ/5EeiGZthXLwwV+qqGGAGGzwbhjz/+QExMDGbPng0zMzNcu3YNLS0tcHV1VTKB1waVlZUQCARwd3fHjh07NE5f6BL5AMLW1lYpgBgBbmC6YuSWjA0V+hLdnvRl+NwfC76R2rwho6ysDObm5gqpH4lEomQCz+FwmG46f39/ODs796udWCqVIikpCSkpKUhISMDcuXN1Gt2yzTuDAtVvMCGkry8WPVFRUUE8PDx6vU8kEhGapgkhhAiFQuLo6Mh835O6ujpSWFhICCGktbWVvPTSS+TmzZsKa3799Vfy2muvEZqmyZUrV0hgYKAWX8nwgqZp0traSn7//XcSHx9Pli5dSjw9Pcm8efPIhx9+SFJTU0l5eTkRi8Wkvb1d6evGjRtk3rx5JDY2lojFYr3s+fLly6SwsFDl7xP7/usFlbrK5nQHAepyxKdPn1b4iJeamqoyUtJkEmt6ejqioqJAURRmzpyJ5uZmhQYRlmdQFAVLS0sEBQUhKCgIQHegIvMdzs/Px5EjR3D//n3weDwmGubz+Th16hROnDiB/fv3Y/bs2XrL3ao7mGXff8PCiu4g4NSpU33eLxAIIBAI+v24rAWfbqAoCg4ODggLC0NYWBiA7hTC7du3IRQKcfbsWcTExCAwMBB5eXkYPVrTVmL9wL7/hoUV3WGKNi34NMkTj9QGDhlcLhfu7u5wd3fHqlWrQAgZ1FUJLIaDFd1hiLYt+IyMjLB3716FUS3BwcFKnsQjvYFDnsEsuKwFo2HRjqEpy6CBaGjBd/z4cRBCkJ+fr7Z5w97enjndls8TswxN+vuF2LIzAAADtUlEQVT+s2gXNtIdZuTl5eHEiRPw8vKCt7c3AGULvudp3lCVJwbYBo7BgiGad1j6QV+lDfqusWAZ3LS1tRFfX1+SlpamdF9LSwtpa2sjhHSXJPF4PH1vb1iTlZVFnJ2diZOTE4mPj1e6Pzk5mYwfP57w+XzC5/PJ0aNHDbBLFjlU6iqbXmDRCHV5YisrK6bDKiQkBBKJBI2NjSofr6OjA4GBgeDz+fDw8MCXX36ptKazsxORkZHg8XiYMWPGiPWnkA1XzcrKQklJCU6dOoWSkhKldZGRkSguLkZxcfGImmY91GBFl0UtRIM8cX19PTMgUZMefRMTE+Tk5OD69esoLi5Gdna2knF8UlISbGxsUFZWhg0bNuCTTz7R3osaQsgPVx01ahQzXJVlaMKKLotaZHninJwceHt7w9vbG5mZmTh06BAOHToEADh9+jQ8PT3B5/MRGxvbZwMH0H26L4uMJRIJJBKJ0vr09HSsXLkSABAREYGLFy/qdfLtYEFVXW1P0tLSMH36dERERChUJ7AMLtiDNBa1vPLKK2rFbiANHFKpFH5+figrK8P69ev7bOIwMjLCmDFj0NTUxNoK9kJ/h6uyGA420mUxGFwuF8XFxaipqUFBQYFGZj99oUmeOCUlBRMmTGAi9u+///65nlMfaFJXO27cOGb2WnR0NAoLC/W6RxbNYUWXxeBYW1tj3rx5yM7OVrhdXmy6urrQ0tLy3HliYOgdOAUEBODOnTuoqKjA06dPkZqaitDQUIU1IpGI+XdGRgbc3Nz0vU0WDWFFl8UgPHjwAM3NzQCAJ0+e4MKFC3B1dVVYExoaimPHjgHozhmrM/zWJE88FJEfrurm5oY33niDGa6akZEBADhw4AA8PDzA5/Nx4MCB5x6uyqJD+qon03tlG8uI4fr168Tb25t4eXkRDw8P8tVXXxFCCPn8889Jeno6IYSQJ0+ekIiICOLk5EQCAgJIeXm52sft6uoifD6fmJubk48//ljp/uTkZGJnZ0e8vLxIeHg4qa6u1u4LY2HpRqWuqjMxZ2EZklAUZQ3gFwAfEEJuyN0+DoCYENJJUdR7ACIJIfMNtU+WkQcruizDFoqivgDwmBCyR8X9XAAPCSFj9LszlpEMm9NlGTZQFDXhrwgXFEWZAQgG8N8ea+SdXUIB3NLfDllY2DpdluGFPYBjf0WwHAA/EULOURS1FcA1QkgGgFiKokIBdAF4COAdg+2WZUTCphdYWFhY9AibXmBhYWHRI6zosrCwsOiR/wcokhEdh+icdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "#导入支持绘制三维图形的第三方包\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "x, y = np.random.rand(2, 100) * 4\n",
    "hist, xedges, yedges = np.histogram2d(x, y, bins=4, range=[[0, 4], [0, 4]])\n",
    "#使用meshgrid函数生产网格\n",
    "xpos, ypos = np.meshgrid(xedges[:-1] + 0.25, yedges[:-1] + 0.25)\n",
    "xpos = xpos.flatten('F')\n",
    "ypos = ypos.flatten('F')\n",
    "zpos = np.zeros_like(xpos)\n",
    "dx = 0.5 * np.ones_like(zpos)\n",
    "dy = dx.copy()\n",
    "dz = hist.flatten()\n",
    "ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
